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AIST 2016, 7-9 April 2016, Yekaterinburg
Conceptual Model for Routine Measurements Processing and Analyses in Adaptive Intelligent Information Systems
Maxim Lapaev, Alexander Vodyaho, Nataly [email protected], [email protected], [email protected]
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Motivation and objectives
The motivation is to provide a user with tools to solve domain-specific tasks
Users are not specialized in data processing issues, especially ones related totemporal measurements as a result of massive data: data is too big; too many interrelations between data pieces; numerous processing methods; methods are specific.
Measurements are the major part of data requiring temporal synchronization (common time scale). Furthermore, measurement data contains noise to be analyzed and eliminated.
A wide diversity of ways to obtain measurements exists nowadays to collectmeasurement data of high quality (precise tools and measuring devices),which provides all the raw data for solving application problems.
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IntroductionCurrent stateRequired featuresOur approach• Baseline• From object to model• Generalization
Model viewsTechnological baseCase study: Botkin’s sheetConclusion
Motivation and objectives
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Currents stateTypical workflow in Federal Almazov North-West Medical Research Center
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Required featuresTasks: To justify expectations an AIIS has to solve following tasks: 1. reduce amount of data; 2. build linked data and information space; 3. enrich data, information and knowledge; 4. provide machine-based applied problems solutions.
Properties: An AIIS must possess following properties: 1. accumulating by gathering all objective and subjective data, information and
knowledge; 2. resource saving; 3. accessibility; 4. theoretical background.
Features:1. intelligence;2. automation 3. dynamism;4. ability to process historical data.
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Our approach: baseline
The proposed concept model is based on a number of general ideas:1. feasible way to investigate the real world through dealing with measurements –
gathering, storing, processing, analyzing;2. capability of consuming measurements is achievable if based on consequent
measurement transformations;3. real-world objects are too complex for modeling, but their numerous views have
simple models;4. real-world processes are poorly predictable, too complex to be formalized, but
well-decomposable into sub-processes.
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Our approach: from object to model
Model principals (a total of 13 principals). Some of the principals are:1. the main value is knowledge; it is vital to operate with knowledge in each case;2. any data can be meaningful; thus, all data is supposed to be carefully processed;3. models and processes must be adaptable at the level of structure and contents level.
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Our approach: from model to general model
To build models we use general models and knowledge domain. Target users are domain experts, end users, researchers and sponsors (model producers)
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Model views
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Technological base1. Transformation technologies: defined for JDL-models for measurements processing.2. Semantic Web technologies: to build interpretable and human- and machine-
comprehensible giant global graph for machine solution of end-users problems. 3. IT technologies: for system design and support using agile technologies provided by IT.
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Case study: Botkin’s temporal sheet
SMDA system prototype for Almazov medical center: http://islegiaa.bget.ru/
Stages:1. obtaining raw measurement data from devices;2. processing separate values and timelines corresponding to value sequences;3. construction of sparse temporal matrix;4. processing sparse matrix to gain event timeline;5. masking sparse matrix by event timeline;6. matrix compression to produce a uniform matrix and event-based intervals;7. calculation of integral patient’s state indicator.
Results:8. a way to asses the correspondence of measurement values with events (medicine
prescriptions, manipulations on patient);9. association of measurements with expected results (typical treatment regimes);10. recommendations for end user (doctor).
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Case study: time- and event-based processing
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Case study: Botkin’s temporal sheet prototype
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Conclusion and future workAlready achieved:1. specification of general models;2. medicine domain-oriented specification;3. a prototype of the system is designed, implemented and passed to domain experts;4. two scenarios are supported: medical (Botkin’s sheet) and managerial (matching
objective (measurements) and subjective (medical notes) data).
Future work: design and implementation of a framework for specialists and non-specialists in domain to deal with models
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Thanks for attention
Maxim Lapaev, Alexander Vodyaho, Nataly [email protected], [email protected], [email protected]
http://www.ifmo.ru/49, Kronverksky Pr., St. Petersburg, 197101, Russia
AIST 2016, 7-9 April 2016, Yekaterinburg